Encouraged by our strong K=500 overclustering results in Table REF , we next evaluate fully unsupervised semantic segmentation.
This relies only on the learned embedding space's structure and refrains from using any test-time label information, the number of final clusters needs to be equivalent to the ground-truth.
To that extent, we start with a simple K-means clustering of the spatial tokens to get cluster assignments for each token.
As prior works, we base our evaluation on PVOC12 val and train self-supervised on an arbitrary dataset [1]}, in this case COCO.
In Table REF  we compare our method to prior state-of-the-art.
We outperform our closest competitor, MaskContrast, by \(>4\%\) .
While like MaskConstrast, we cluster only foreground tokens, we use our embedding space clustering instead of a pretrained unsupervised saliency estimator to do cluster-based foreground extraction (CBFE).
Also, instead of averaging the feature representations per image, we use our novel unsupervised overclustering method with community detection (CD), allowing us to detect multiple object categories in one image.
